Atrial fibrillation (AF) is the most common arrhythmia and affects over 2 million Americans. AF is a major public health burden as it is associated with a five-fold increased stroke risk, a tripling in heart failure risk and nearly two-fold increase in mortality. There is increasing evidence of a genetic component to AF, and mutations for AF have been described in ion channels and signaling molecules, but such mutations are rare. Genome-wide association studies (GWAS) have uncovered many common variants underlying risk for a wide range of diseases. In 2007, a GWAS for AF in Icelanders identified a susceptibility region for AF on chromosome (chr) 4q25, findings that were replicated in our population with lone AF.1 While the chr 4q25 locus is in important development in our understanding of AF, these findings were based on an initial GWAS of only 550 individuals with AF. Since there are likely many other genetic variants for AF, Drs. Ellinor, Benjamin and Heckbert organized the CHARGE-AF consortium consisting of investigators from 12 studies with over 8,000 subjects with AF and 86,000 subjects without AF. Subjects with lone AF and no evidence of structural heart disease have a particularly high familial aggregation of AF. In preliminary work, we performed a GWAS of lone AF using cases from five studies;a total of 1,335 cases with lone AF and 12,844 referent subjects were available. At chr 4q25, 77 single nucleotide polymorphisms (SNPs) had P<5x10-8. A second, novel locus was identified on chr 1q21;the most significant SNP, rs13376333, is intronic to the potassium channel KCNN3. In a meta-analysis of the primary and replication cohorts, rs13376333 had an odds ratio of 1.52 (P=1.8x10-21). KCNN3 is a member of a family of voltage-independent calcium-activated potassium channels expressed in a number of excitable tissues including the brain40 and the heart;41, 42 however, the role of these channels in the heart is less clear. We propose to extend our work through the following specific aims:
Aim 1 - Determine if genetic variation in KCNN3 is associated with AF risk by fine mapping the KCNN3 locus, correlating left atrial RNA levels with KCNN3 genotypes, and determining if a polyglutamine repeat in KCNN3 is associated with AF.
Aim 2 - Identify and characterize mutations and rare variants in KCNN3 in subjects with AF.
Aim 3 - Identify conserved, non-coding regulatory elements associated with KCNN3 function.
Aim 4 - Characterize the cardiac phenotypes of two mouse lines with alterations in KCNN3 function. We believe that our multidisciplinary translational approach integrating available GWAS, cellular electrophysiology, and animal model systems, is uniquely suited to pursue this project. Understanding the molecular determinants of AF will provide insights into the pathogenesis of AF, and eventually provide targets for new therapies to prevent and treat this morbid arrhythmia.

Public Health Relevance

Atrial fibrillation is the most common irregular heart rhythm and leads to an increased risk of stroke, heart failure, dementia, and death. In a genetic study, investigators have recently identified a gene, KCNN3, associated with an increased risk of atrial fibrillation. We propose to examine the role of KCNN3 in atrial fibrillation using a combination of human studies from the United States and Europe, cell lines, and mouse models with alterations in KCNN3 function.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL104156-03
Application #
8260244
Study Section
Clinical and Integrative Cardiovascular Sciences Study Section (CICS)
Program Officer
Wang, Lan-Hsiang
Project Start
2010-08-01
Project End
2014-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
3
Fiscal Year
2012
Total Cost
$537,671
Indirect Cost
$196,439
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Lin, Honghuang; Mueller-Nurasyid, Martina; Smith, Albert V et al. (2016) Gene-gene Interaction Analyses for Atrial Fibrillation. Sci Rep 6:35371
Khurshid, Shaan; Keaney, John; Ellinor, Patrick T et al. (2016) A Simple and Portable Algorithm for Identifying Atrial Fibrillation in the Electronic Medical Record. Am J Cardiol 117:221-5
Rahman, Faisal; Wang, Na; Yin, Xiaoyan et al. (2016) Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study. Heart Rhythm 13:233-40
Rahman, Faisal; Yin, Xiaoyan; Larson, Martin G et al. (2016) Trajectories of Risk Factors and Risk of New-Onset Atrial Fibrillation in the Framingham Heart Study. Hypertension 68:597-605
Ma, Ji-Fang; Yang, Fan; Mahida, Saagar N et al. (2016) TBX5 mutations contribute to early-onset atrial fibrillation in Chinese and Caucasians. Cardiovasc Res 109:442-50
Choi, Eue-Keun; Park, Jae Hyung; Lee, Ji-Young et al. (2015) Korean Atrial Fibrillation (AF) Network: Genetic Variants for AF Do Not Predict Ablation Success. J Am Heart Assoc 4:e002046
Lubitz, Steven A; Yin, Xiaoyan; Rienstra, Michiel et al. (2015) Long-term outcomes of secondary atrial fibrillation in the community: the Framingham Heart Study. Circulation 131:1648-55
Lubitz, Steven A; Ellinor, Patrick T (2015) Somatic mutations and atrial fibrillation: the end or just the beginning? Circ Cardiovasc Genet 8:2-3
Dina, Christian; Bouatia-Naji, Nabila; Tucker, Nathan et al. (2015) Genetic association analyses highlight biological pathways underlying mitral valve prolapse. Nat Genet 47:1206-11
Jabbari, Javad; Olesen, Morten S; Yuan, Lei et al. (2015) Common and rare variants in SCN10A modulate the risk of atrial fibrillation. Circ Cardiovasc Genet 8:64-73

Showing the most recent 10 out of 82 publications